首记
感觉Hadoop是一个坑，打着大数据最佳解决方案的旗帜到处坑害良民。记得以前看过一篇文章，说1TB以下的数据就不要用Hadoop了，体现不出太大的优势，有时候反而会成为累赘。因此Hadoop的使用场所一般有两：一是有一定规模的公司，数据流一般是TB级别的，这样的公司其实不多；二是各大高校的实验室，作为研究使用。不幸的我也走上了这条路，仅为研究之用。而且我的使用需求还不是一般的在Hadoop下开发应用程序，而是开发好的C++程序要放到Hadoop平台下进行测试。Hadoop是基于Java的数据计算平台，当然对Java支持的最好，如果要运行C++程序，有三种解决方案：
- 使用JNI/JNA/JNR技术。这三种Java外部函数接口技术都是解决在Java程序中运行C++功能函数的需求，从而使得在Hadoop平台下开发Java程序且能调用C++函数完成在Hadoop Java版应用中运行C++程序的目的。早期（大概11年）阿里就是使用该技术将C语言实现的分词软件成功部署到Hadoop平台下运行，详情请看参考资料1。用过JNI的都知道JNI实在不好用，所以后来有人开发了另外两种Java外部函数接口，即JNA和JNR，具体介绍和使用实例请看参考资料2和参考资料3。
- 使用Hadoop Streaming技术。这项技术可以使得除了Java之外的多种其它语言如C/C++/Python/C#甚至shell脚本等运行在Hadoop平台下，程序只需要按照一定的格式从标准输入读取数据、向标准输出写数据就可以在Hadoop平台上使用，原有的单机程序稍加改动就可以在Hadoop平台进行分布式处理。
- 使用Hadoop Pipes技术。该技术只专注于在Hadoop平台下运行C++程序，只允许用户使用C++语言进行MapReduce程序设计。它采用的主要方法是将应用逻辑相关的C++代码放在单独的进程中，然后通过Socket让Java代码与C++代码通信。从很大程度上说，这种方法类似于Hadoop Streaming，不同之处是通信方式不同：一个是标准输入输出，另一个是socket。
对于这三种技术我都做了相关调研和比较分析，首先排除了方法1，因为我不想写Java程序来调用C++功能，有些累赘，调试非常不方便，那么是用Hadoop Streaming技术还是Hadoop Pipes技术呢？两种方式各有优缺点，具体可看参考资料4（说的不太准确，仅供参考），为了准确选择，我需要将两种方法都试验一下，看哪个适合自己的需求再做最终决定。
首先选择了仅专注C++的Hadoop Pipes技术，于是就开启了下面一系列的过程……
Hadoop 2.3环境配置安装
我之前配置过Hadoop环境（看参考资料5），但那时用的版本是1.1.2，比较老的版本，一堆bug，为了避免遗留bug的困扰我选择了最新版2.3，因此需要重新配置（主要借鉴参考资料6），由于2.3版本使用的是新MapReduce框架yarn，因此配置与之前有所差异。
配置成功后，用自带的经典案例wordcount测试下运行是否正常（已上传数据）：
hadoop jar ./hadoop-2.3.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.3.0.jar wordcount wc_input.txt out
结果出现了下面错误：
2014-04-03 21:19:40,847 FATAL org.apache.hadoop.yarn.server.nodemanager.NodeManager: Error starting NodeManagerorg.apache.hadoop.yarn.exceptions.YarnRuntimeException: java.net.ConnectException: Call From Slave1/192.168.1.152 to 0.0.0.0:8031 failed on connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org/hadoop/ConnectionRefusedat org.apache.hadoop.yarn.server.nodemanager.NodeStatusUpdaterImpl.serviceStart(NodeStatusUpdaterImpl.java:185)at org.apache.hadoop.service.AbstractService.start(AbstractService.java:193)at org.apache.hadoop.service.CompositeService.serviceStart(CompositeService.java:121)at org.apache.hadoop.yarn.server.nodemanager.NodeManager.serviceStart(NodeManager.java:199)at org.apache.hadoop.service.AbstractService.start(AbstractService.java:193)at org.apache.hadoop.yarn.server.nodemanager.NodeManager.initAndStartNodeManager(NodeManager.java:354)at org.apache.hadoop.yarn.server.nodemanager.NodeManager.main(NodeManager.java:401)Caused by: java.net.ConnectException: Call From Slave1/192.168.1.152 to 0.0.0.0:8031 failed on connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org/hadoop/ConnectionRefusedat sun.reflect.GeneratedConstructorAccessor8.newInstance(Unknown Source)at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)at java.lang.reflect.Constructor.newInstance(Constructor.java:526)at org.apache.hadoop.net.NetUtils.wrapWithMessage(NetUtils.java:783)at org.apache.hadoop.net.NetUtils.wrapException(NetUtils.java:730)at org.apache.hadoop.ipc.Client.call(Client.java:1410)at org.apache.hadoop.ipc.Client.call(Client.java:1359)at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:206)at com.sun.proxy.$Proxy23.registerNodeManager(Unknown Source)at org.apache.hadoop.yarn.server.api.impl.pb.client.ResourceTrackerPBClientImpl.registerNodeManager(ResourceTrackerPBClientImpl.java:68)at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source)at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)at java.lang.reflect.Method.invoke(Method.java:606)at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:186)at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)at com.sun.proxy.$Proxy24.registerNodeManager(Unknown Source)at org.apache.hadoop.yarn.server.nodemanager.NodeStatusUpdaterImpl.registerWithRM(NodeStatusUpdaterImpl.java:247)at org.apache.hadoop.yarn.server.nodemanager.NodeStatusUpdaterImpl.serviceStart(NodeStatusUpdaterImpl.java:179)... 6 moreCaused by: java.net.ConnectException: Connection refusedat sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:708)at org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:206)at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:529)at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:493)at org.apache.hadoop.ipc.Client$Connection.setupConnection(Client.java:601)at org.apache.hadoop.ipc.Client$Connection.setupIOstreams(Client.java:696)at org.apache.hadoop.ipc.Client$Connection.access$2700(Client.java:367)at org.apache.hadoop.ipc.Client.getConnection(Client.java:1458)at org.apache.hadoop.ipc.Client.call(Client.java:1377)... 18 more
是连接拒绝问题，连接了“0.0.0.0:8031”这个地址，这显然是某个选项没配置连接地址，导致使用了默认的错误地址。经过查资料，是yarn-site.xml文件没配置好，其中的yarn.nodemanager.address没有配置，默认是0.0.0.0。正确的配置如下（可能有些项不需要配置，但为了保险还是大部分都配置了）
<configuration>然后就运行成功了。开始以为环境就这样配好了，直到我测试Hadoop pipes程序……<property><name>yarn.resourcemanager.address</name><value>192.168.1.137:8032</value></property><property><name>yarn.resourcemanager.scheduler.address</name><value>192.168.1.137:8030</value></property><property><name>yarn.resourcemanager.resource-tracker.address</name><value>192.168.1.137:8031</value></property><property><name>yarn.resourcemanager.admin.address</name><value>192.168.1.137:8033</value></property><property><name>yarn.resourcemanager.webapp.address</name><value>192.168.1.137:8088</value></property><property><name>yarn.nodemanager.aux-services</name><value>mapreduce_shuffle</value></property><property><name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name><value>org.apache.hadoop.mapred.ShuffleHandler</value></property></configuration>
Hadoop pipes运行错误集锦
Hadoop 2.3发布版本没有自带Hadoop pipes的wordcount例子，于是我就在老的1.1.2版本中找来了wordcount-simple.cc例子，修改了头文件路径（新版本与旧版本路径完全不一样），并参考资料7使用下面的makefile编译（其中的-lssl也许非必需）：
HADOOP_INSTALL=/opt/hadoop CC = g++ CCFLAGS = -I$(HADOOP_INSTALL)/include wordcount :wordcount-simple.cc $(CC) $(CCFLAGS) $< -Wall -L$(HADOOP_INSTALL)/lib/native -lhadooppipes -lhadooputils -lpthread -lcrypto -lssl -g -O2 -o $@编译成功后，上传到HDFS上，用下面的命令测试运行：
hadoop pipes -D hadoop.pipes.java.recordreader=true -D hadoop.pipes.java.recordwriter=true -D mapred.job.name=wordcount -input /data/wc_in -output /data/wc_out2 -program /bin/wordcount开始一直卡在”map 0% reduce 0%“，不知道过了多久，报出了下面一系列错误：
DEPRECATED: Use of this script to execute mapred command is deprecated.通过网上各种资料（之所以在网上找解决方案是因为在namenode、datanode及yarn的日志里都没能找到错误信息及有效信息，所以很不解到底是什么原因导致的错误），解决方案都不靠谱，都没能解决该问题。最后自己一阵捣鼓，最终在一个很隐蔽的日志里看到了错误提示，该日志在datanode的Hadoop安装路径下logs目录下的userlogs文件夹里，开始以为这是个无用的文件夹，后来发现这个文件夹解决了我所有的问题，该文件夹下面包含了很多application子文件夹，而子文件夹下面又包含了很多container子文件夹，container下面就是标准的三个输出日志”stderr“、”stdout“和”syslog“，也就是这样的目录结构（datanode的container logs）：Instead use the mapred command for it.14/04/03 23:59:48 INFO client.RMProxy: Connecting to ResourceManager at /192.168.1.137:803214/04/03 23:59:49 INFO client.RMProxy: Connecting to ResourceManager at /192.168.1.137:803214/04/03 23:59:50 WARN mapreduce.JobSubmitter: No job jar file set. User classes may not be found. See Job or Job#setJar(String).14/04/03 23:59:50 INFO mapred.FileInputFormat: Total input paths to process : 214/04/03 23:59:51 INFO mapreduce.JobSubmitter: number of splits:214/04/03 23:59:51 INFO Configuration.deprecation: hadoop.pipes.java.recordreader is deprecated. Instead, use mapreduce.pipes.isjavarecordreader14/04/03 23:59:51 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name14/04/03 23:59:51 INFO Configuration.deprecation: hadoop.pipes.java.recordwriter is deprecated. Instead, use mapreduce.pipes.isjavarecordwriter14/04/03 23:59:52 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1396578697573_000414/04/03 23:59:52 INFO mapred.YARNRunner: Job jar is not present. Not adding any jar to the list of resources.14/04/03 23:59:53 INFO impl.YarnClientImpl: Submitted application application_1396578697573_000414/04/03 23:59:53 INFO mapreduce.Job: The url to track the job: http://Master:8088/proxy/application_1396578697573_0004/14/04/03 23:59:53 INFO mapreduce.Job: Running job: job_1396578697573_000414/04/04 00:00:26 INFO mapreduce.Job: Job job_1396578697573_0004 running in uber mode : false14/04/04 00:00:26 INFO mapreduce.Job: map 0% reduce 0%14/04/04 00:10:53 INFO mapreduce.Job: map 100% reduce 0%14/04/04 00:10:53 INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000001_0, Status : FAILEDAttemptID:attempt_1396578697573_0004_m_000001_0 Timed out after 600 secs14/04/04 00:10:54 INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000000_0, Status : FAILEDAttemptID:attempt_1396578697573_0004_m_000000_0 Timed out after 600 secs14/04/04 00:10:55 INFO mapreduce.Job: map 0% reduce 0%14/04/04 00:21:23 INFO mapreduce.Job: map 100% reduce 0%14/04/04 00:21:24 INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000000_1, Status : FAILEDAttemptID:attempt_1396578697573_0004_m_000000_1 Timed out after 600 secs14/04/04 00:21:24 INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000001_1, Status : FAILEDAttemptID:attempt_1396578697573_0004_m_000001_1 Timed out after 600 secs14/04/04 00:21:25 INFO mapreduce.Job: map 0% reduce 0%14/04/04 00:31:53 INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000000_2, Status : FAILEDAttemptID:attempt_1396578697573_0004_m_000000_2 Timed out after 600 secs14/04/04 00:31:53 INFO mapreduce.Job: Task Id : attempt_1396578697573_0004_m_000001_2, Status : FAILEDAttemptID:attempt_1396578697573_0004_m_000001_2 Timed out after 600 secs14/04/04 00:42:24 INFO mapreduce.Job: map 100% reduce 0%14/04/04 00:42:25 INFO mapreduce.Job: map 100% reduce 100%14/04/04 00:42:26 INFO mapreduce.Job: Job job_1396578697573_0004 failed with state FAILED due to: Task failed task_1396578697573_0004_m_000000Job failed as tasks failed. failedMaps:1 failedReduces:014/04/04 00:42:27 INFO mapreduce.Job: Counters: 9Job CountersFailed map tasks=8Launched map tasks=8Other local map tasks=6Data-local map tasks=2Total time spent by all maps in occupied slots (ms)=5017539Total time spent by all reduces in occupied slots (ms)=0Total time spent by all map tasks (ms)=5017539Total vcore-seconds taken by all map tasks=5017539Total megabyte-seconds taken by all map tasks=5137959936Exception in thread "main" java.io.IOException: Job failed!at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:836)at org.apache.hadoop.mapred.pipes.Submitter.runJob(Submitter.java:264)at org.apache.hadoop.mapred.pipes.Submitter.run(Submitter.java:503)at org.apache.hadoop.mapred.pipes.Submitter.main(Submitter.java:518)
|-- Hadoop安装路径
|-- logs
|- -userlogs
|-- application_XXXXXXXX
|-- container_XXXXXXXX
|-- stderr
|-- stdout
|-- syslog
其中stderr是最重要的文件，具体的错误原因就保存在这个文件里面。比如上面的错误信息在该文件里找到了这样的错误提示（...表示省略无关信息）：
.../application_1396607014314_0001/container_1396607014314_0001_01_000002/wordcount: error while loading shared libraries: libcrypto.so.10: cannot open shared object file: No such file or directory在datanode上用”locate libcrypto.so.10“搜了下的确没找到，但在namenode上找到了这个共享库文件，于是就将namenode下的该文件拷贝到了datanode的/usr/lib下面，重新运行程序，还是同样的问题，卡住了最后报出了那样的错误，于是到datanode下面的container logs里找原因，发现了新的问题：
/usr/lib/libstdc++.so.6: version `GLIBCXX_3.4.11‘ not found (required by .../wordcount)
同样的还是namenode里有而datanode里没有，因此还是将namenode的libstdc++.so.6拷过去了，再次运行，错误依旧，这时原因是：
error while loading shared libraries: /usr/lib/libstdc++.so.6: ELF file OS ABI invalid
这时候我没有去搜相应的解决方案，我似乎知道了问题的症结所在，每次问题都是datanode没有相关的库文件而namenode都有，将namenode上的拷过去还可能造成与系统不匹配无效的后果，那么问题就应该出在：namenode(master)和datanode(slave)云节点系统内核的不匹配。我的master节点是Ubuntu 13.04而slave节点是特别旧的Ubuntu
9.11，虽然都是Ubuntu但是内核已经不一样，各个库文件已经得到升级和改动，而Hadoop在运行程序时需要master和slave之间的通信互动，采用的请求机制是一样的，比如master在请求一个动作时用到了libcrypto.so.10这个版本的库文件，那么slave答复或请求master时也会去自己的系统上找这样的库文件，结果自己的系统上没有，根据Hadoop运行机制就有个timeout和继续重新请求的过程，但是一直请求不到，所以就一直卡在那个地方，直到最终在限定的时间里没有完成任务导致的”Job failed“。可想而知，如果你master用的是Ubuntu而slave用的是Fedora，那么这样的错误也非常可能发生。那么为什么之前运行普通的Java版wordcount程序没有发生错误呢？这我就不清楚了，毕竟不知道其具体的内部运行机理。我猜测可能是Hadoop pipes采用的是socket通信方式，所以需要用到那些库文件，而普通的Java版程序不需要这样的过程，因此没有发生错误，当然也不排除运行稍复杂的涉及通信的Java程序也发生这样的错误。
因此，我也得出这样一个结论：
Hadoop环境的master节点和所有slave节点的系统环境最好一模一样（同一个系统同一个版本号），对于Hadoop pipes而言就是必须一模一样。
由于我用的都是虚拟机系统，因此需要环境一样的最简单方式就是——克隆（clone）虚拟机，使用链接link方式即可。然后重新配置Hadoop 2.3环境，重新运行，以为问题就此解决，可是新的问题又来了：
14/04/06 01:11:49 INFO mapreduce.Job: Running job: job_1396756477966_0002注意这里并没有一直卡在”map 0% reduce 0%“而是很快返回了错误结果，说明之前的问题已经解决，这次肯定不是系统的问题，而是IO问题。同样检查container logs，发现了问题原因：14/04/06 01:12:04 INFO mapreduce.Job: Job job_1396756477966_0002 running in uber mode : false14/04/06 01:12:04 INFO mapreduce.Job: map 0% reduce 0%14/04/04 09:59:02 INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000000_0, Status : FAILEDError: java.io.IOExceptionat org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:186)at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:195)at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:150)at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:69)at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:430)at org.apache.hadoop.mapred.MapTask.run(MapTask.java:342)at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)at java.security.AccessController.doPrivileged(Native Method)at javax.security.auth.Subject.doAs(Subject.java:415)at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)14/04/04 09:59:02 INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000001_0, Status : FAILEDError: java.io.IOExceptionat org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:186)at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:195)at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:150)at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:69)at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:430)at org.apache.hadoop.mapred.MapTask.run(MapTask.java:342)at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)at java.security.AccessController.doPrivileged(Native Method)at javax.security.auth.Subject.doAs(Subject.java:415)at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)14/04/04 09:59:14 INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000000_1, Status : FAILEDError: java.io.IOExceptionat org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:186)at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:195)at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:150)at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:69)at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:430)at org.apache.hadoop.mapred.MapTask.run(MapTask.java:342)at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)at java.security.AccessController.doPrivileged(Native Method)at javax.security.auth.Subject.doAs(Subject.java:415)at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)14/04/04 09:59:15 INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000001_1, Status : FAILEDError: java.io.IOExceptionat org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:186)at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:195)at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:150)at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:69)at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:430)at org.apache.hadoop.mapred.MapTask.run(MapTask.java:342)at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)at java.security.AccessController.doPrivileged(Native Method)at javax.security.auth.Subject.doAs(Subject.java:415)at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)14/04/04 09:59:26 INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000000_2, Status : FAILEDError: java.io.IOExceptionat org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:186)at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:195)at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:150)at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:69)at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:430)at org.apache.hadoop.mapred.MapTask.run(MapTask.java:342)at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)at java.security.AccessController.doPrivileged(Native Method)at javax.security.auth.Subject.doAs(Subject.java:415)at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)14/04/04 09:59:27 INFO mapreduce.Job: Task Id : attempt_1396618478715_0002_m_000001_2, Status : FAILEDError: java.io.IOExceptionat org.apache.hadoop.mapred.pipes.OutputHandler.waitForAuthentication(OutputHandler.java:186)at org.apache.hadoop.mapred.pipes.Application.waitForAuthentication(Application.java:195)at org.apache.hadoop.mapred.pipes.Application.<init>(Application.java:150)at org.apache.hadoop.mapred.pipes.PipesMapRunner.run(PipesMapRunner.java:69)at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:430)at org.apache.hadoop.mapred.MapTask.run(MapTask.java:342)at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)at java.security.AccessController.doPrivileged(Native Method)at javax.security.auth.Subject.doAs(Subject.java:415)at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)14/04/04 09:59:40 INFO mapreduce.Job: map 100% reduce 100%14/04/04 09:59:41 INFO mapreduce.Job: Job job_1396618478715_0002 failed with state FAILED due to: Task failed task_1396618478715_0002_m_000000Job failed as tasks failed. failedMaps:1 failedReduces:014/04/04 09:59:41 INFO mapreduce.Job: Counters: 13Job CountersFailed map tasks=7Killed map tasks=1Launched map tasks=8Other local map tasks=6Data-local map tasks=2Total time spent by all maps in occupied slots (ms)=88669Total time spent by all reduces in occupied slots (ms)=0Total time spent by all map tasks (ms)=88669Total vcore-seconds taken by all map tasks=88669Total megabyte-seconds taken by all map tasks=90797056Map-Reduce FrameworkCPU time spent (ms)=0Physical memory (bytes) snapshot=0Virtual memory (bytes) snapshot=0Exception in thread "main" java.io.IOException: Job failed!at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:836)at org.apache.hadoop.mapred.pipes.Submitter.runJob(Submitter.java:264)at org.apache.hadoop.mapred.pipes.Submitter.run(Submitter.java:503)at org.apache.hadoop.mapred.pipes.Submitter.main(Submitter.java:518)
Server failed to authenticate. Exiting
然后搜索解决方案，最后发现原因竟然是Hadoop 2.3自身的原因，需要我们自己编译Makefile里所需要的libhadooppipes.a和libhadooputils.a这两个静态库文件以适应自己系统的需求（官网说预编译的是32位库，如果你是64位的才需要重新编译，可是我主机虽是64位但虚拟机系统是32位的，不知道为什么也不行需要重新编译）。这个要求真是很不注重用户体验的要求，于是就来到了重新编译Hadoop 2.3本地库的地狱世界……
编译Hadoop 2.3 Native Library
首先当然是先下载Hadoop 2.3的源码了，解压后根据里面的BUILDING.txt使用下面命令进行编译获得本地库：
期间出现了各种错误，基本上所有的错误都出现在参考资料9中，解决方案就是安装编译前所需要的各种依赖库和依赖程序，比如protobuf、cmake、zlib-devel和openssl-devel，都安装好了过后就”BUILD SUCCESS“了，真是不易，其实官网也给出了这些提示，说需要提前安装所依赖的东西，只是一开始懒得看，具体看参考资料10。mvn package -Pdist,native -Dskiptests -Dtar
编译成功后，在
路径下可以看到我们所需要的libhadooppipes.a和libhadooputils.a了，将它们拷贝到master和所有slave机器上的lib/native文件里（最好事先备份一下自带的库文件，避免该方案失败），然后重新make编译wordcount程序，然后Hadoop pipes运行，最终看到这样的结果：HADOOP_PATH/hadoop-tools/hadoop-pipes/target/native/
14/04/06 01:22:27 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 14/04/06 01:22:28 INFO client.RMProxy: Connecting to ResourceManager at /192.168.1.137:8032 14/04/06 01:22:28 INFO client.RMProxy: Connecting to ResourceManager at /192.168.1.137:8032 14/04/06 01:22:29 WARN mapreduce.JobSubmitter: No job jar file set. User classes may not be found. See Job or Job#setJar(String). 14/04/06 01:22:29 INFO mapred.FileInputFormat: Total input paths to process : 2 14/04/06 01:22:30 INFO mapreduce.JobSubmitter: number of splits:2 14/04/06 01:22:30 INFO Configuration.deprecation: hadoop.pipes.java.recordreader is deprecated. Instead, use mapreduce.pipes.isjavarecordreader 14/04/06 01:22:30 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name 14/04/06 01:22:30 INFO Configuration.deprecation: hadoop.pipes.java.recordwriter is deprecated. Instead, use mapreduce.pipes.isjavarecordwriter 14/04/06 01:22:30 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1396756477966_0004 14/04/06 01:22:31 INFO mapred.YARNRunner: Job jar is not present. Not adding any jar to the list of resources. 14/04/06 01:22:31 INFO impl.YarnClientImpl: Submitted application application_1396756477966_0004 14/04/06 01:22:31 INFO mapreduce.Job: The url to track the job: http://Master:8088/proxy/application_1396756477966_0004/ 14/04/06 01:22:31 INFO mapreduce.Job: Running job: job_1396756477966_0004 14/04/06 01:22:40 INFO mapreduce.Job: Job job_1396756477966_0004 running in uber mode : false 14/04/06 01:22:40 INFO mapreduce.Job: map 0% reduce 0% 14/04/06 01:22:56 INFO mapreduce.Job: map 67% reduce 0% 14/04/06 01:22:57 INFO mapreduce.Job: map 100% reduce 0% 14/04/06 01:23:09 INFO mapreduce.Job: map 100% reduce 100% 14/04/06 01:23:11 INFO mapreduce.Job: Job job_1396756477966_0004 completed successfully 14/04/06 01:23:12 INFO mapreduce.Job: Counters: 51 File System Counters FILE: Number of bytes read=118 FILE: Number of bytes written=260641 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=266 HDFS: Number of bytes written=86 HDFS: Number of read operations=9 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=2 Launched reduce tasks=1 Data-local map tasks=2 Total time spent by all maps in occupied slots (ms)=30171 Total time spent by all reduces in occupied slots (ms)=10914 Total time spent by all map tasks (ms)=30171 Total time spent by all reduce tasks (ms)=10914 Total vcore-seconds taken by all map tasks=30171 Total vcore-seconds taken by all reduce tasks=10914 Total megabyte-seconds taken by all map tasks=30895104 Total megabyte-seconds taken by all reduce tasks=11175936 Map-Reduce Framework Map input records=8 Map output records=9 Map output bytes=94 Map output materialized bytes=124 Input split bytes=190 Combine input records=0 Combine output records=0 Reduce input groups=8 Reduce shuffle bytes=124 Reduce input records=9 Reduce output records=8 Spilled Records=18 Shuffled Maps =2 Failed Shuffles=0 Merged Map outputs=2 GC time elapsed (ms)=764 CPU time spent (ms)=2490 Physical memory (bytes) snapshot=384413696 Virtual memory (bytes) snapshot=3685318656 Total committed heap usage (bytes)=258613248 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 WORDCOUNT INPUT_WORDS=9 OUTPUT_WORDS=8 File Input Format Counters Bytes Read=76 File Output Format Counters Bytes Written=86 14/04/06 01:23:12 INFO util.ExitUtil: Exiting with status 0
心里真是五味杂陈，这个配置环境的过程真是太痛苦了，当然也锻炼了发现问题和解决问题的能力，也懂得了遇到问题后，LOG永远是最佳解决途径。
谨以此文来记录Hadoop pipes运行环境的配置经历，同样也给那些与我遇到类似问题的小伙伴们指明一条明路……
参考资料
3.JNI的又一替代者—使用JNR访问Java外部函数接口(jnr-ffi)
7. Hadoop Tutorial 2.2 -- Running C++ Programs on Hadoop
8. Hadoop 新 MapReduce 框架 Yarn 详解
在Hadoop 2.3上运行C++程序各种疑难杂症（Hadoop Pipes选择、错误集锦、Hadoop2.3编译等）,布布扣,bubuko.com